Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market

Adam Korniejczuk and Robert Ślepaczuk
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Adam Korniejczuk: University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group

No 2024-09, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of machine learning classifiers have been used to improve risk-adjusted returns and increase the immunity to transaction costs over existing approaches. The study seeks to provide an integrated approach to optimal signal detection and risk management. As a part of this approach, innovative ways of optimizing take profit and stop loss functions for daily frequency trading strategies have been proposed and tested. All of the tested approaches outperformed appropriate benchmarks. The best combinations of the techniques and parameters demonstrated significantly better performance metrics than the relevant benchmarks. The results have been obtained under the assumption of realistic transaction costs, but are sensitive to the changes of some key parameters.

Keywords: graph clustering algorithms; statistical arbitrage; algorithmic investment strategies; pair trading strategy; Kelly criterion; machine learning; risk adjusted returns (search for similar items in EconPapers)
JEL-codes: C4 C45 C55 C65 G11 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2024
New Economics Papers: this item is included in nep-big and nep-cmp
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https://www.wne.uw.edu.pl/download_file/4275/0 First version, 2024 (application/pdf)

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Working Paper: Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2024-09

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